Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering that when faced with large-scale sentiment analysis tasks,the high accuracy rate often requires long experimental time.In view of the weakness,a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed,and singular value decomposition(SVD)is used to find the sparse feature of the image,which are sparse vectors with strong discriminativeness and effectively reduce redundant information;The authors propose the Fast Dictionary Learning algorithm(FDL),which can combine neural network with sparse representation.This method is based on K-Singular Value Decomposition,and through iteration,it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy.Moreover,the effectiveness of the proposed method is evaluated on the FER2013 dataset.By adding singular value decomposition,the accuracy of the test suite increased by 0.53%,and the total experiment time was shortened by 8.2%;Fast Dictionary Learning shortened the total experiment time by 36.3%.
展开▼
机译：The Metabolic Network of Bacillus subtilis by Constraints Based Reconstruction and Analysis (COBRA)AU Bui, Bin (1485507359@qq.com) Li, Gang Wei, CongChong Fan, Yadong Wang, Yongkang Wang, Wenfeng Xin, Weichen Yang, Yi Wen, Han
机译：Data mining of Arabidopsis thaliana salt-response proteins based on bioinformatics analysis Guo Meili, Gao Weixi, Yu Xuejuan, Zhou Chunxi, Liu Fujun, Liu Xin Abstract
机译：metodi microbiologici Tradizionali e metodi moleculeolari per l'analisi degli integratori alimentari a base di o con probiotici per ujso umano（microbiological and molecular methods for analysis of probiotic Based Food supplements for Human Consumption）。
